Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis

Simon Haworth, Ruth Mitchell, Laura Corbin, Kaitlin H. Wade, Tom Dudding, Ashley Budu-Aggrey, David Carslake, Gibran Hemani, Lavinia Paternoster, George Davey Smith, Neil Davies, Daniel J. Lawson and Nicholas Timpson ()
Additional contact information
Simon Haworth: University of Bristol
Ruth Mitchell: University of Bristol
Laura Corbin: University of Bristol
Kaitlin H. Wade: University of Bristol
Tom Dudding: University of Bristol
Ashley Budu-Aggrey: University of Bristol
David Carslake: University of Bristol
Gibran Hemani: University of Bristol
Lavinia Paternoster: University of Bristol
George Davey Smith: University of Bristol
Neil Davies: University of Bristol
Daniel J. Lawson: University of Bristol
Nicholas Timpson: University of Bristol

Nature Communications, 2019, vol. 10, issue 1, 1-9

Abstract: Abstract Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multiple variants are associated with birth location within UK Biobank and that geographic structure in genotype data cannot be accounted for using routine adjustment for study centre and principal components derived from genotype data. We find that major health outcomes appear geographically structured and that coincident structure in health outcomes and genotype data can yield biased associations. Understanding and accounting for this phenomenon will be important when making inference from genotype data in large studies.

Date: 2019
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DOI: 10.1038/s41467-018-08219-1

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